From Web to Insights: Automating and Optimizing Job Data Collection with Selenium

Pramiti Tewari¹ , Utkarsh Gupta² , Samriddhi Tripathi³ , Ajay Kumar⁴

Section:Research Paper, Product Type: Journal-Paper
Vol.11 , Issue.4 , pp.1-7, Dec-2024

Online published on Dec 31, 2024

Copyright © Pramiti Tewari, Utkarsh Gupta, Samriddhi Tripathi, Ajay Kumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
The research explores web scraping as an efficient method for data extraction, focusing on job postings from LinkedIn using Selenium. By automating the interactions with dynamic web elements, the study extracts job data such as job titles, companies, and addresses from the recent job postings. It also addresses the challenges such as dynamic content handling, anti-bot mechanisms while also keeping the legal norms and ethical considerations of data mining. Python modules such as BeautifulSoup, Scrapy and Selenium are reviewed as choices for the automated script while emphasis is given on Selenium’s scalability, robustness, efficiency and adaptability to real-world scenarios such as multi-page navigation, error handling and regular updates. The approach highlights web scraping’s potential in leveraging data mining and potential for effective analysis, offering an ethical solution for data-driven approach.

Key-Words / Index Term :
Web Scraping, Selenium, Python Automation, Dynamic Content Handling, Pagination, WebDriver

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